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        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.21

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        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/sarek analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2025-02-18, 22:04 CET based on data in: /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/nfcore_sarek_rerun


        General Statistics

        Showing 96/96 rows and 18/30 columns.
        Sample Name% DuplicationM Reads After FilteringGC content% PFDuplicationError rateNon-primaryReads mapped% Mapped% Proper pairsTotal seqs≥ 30XMedianMean Cov.VarsSNPIndelTs/Tv
        aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-1
        1.3%
        216.0M
        41.1%
        100.0%
        aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-2
        1.0%
        167.1M
        41.2%
        100.0%
        aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-3
        0.9%
        157.1M
        41.2%
        100.0%
        aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-3.KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-3_1
        1.0%
        aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0478-OBPA_KMS-812-5mil-DE090-2A1A-1
        1.3%
        214.9M
        41.2%
        100.0%
        aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0478-OBPA_KMS-812-5mil-DE090-2A1A-2
        1.0%
        166.4M
        41.2%
        100.0%
        aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0478-OBPA_KMS-812-5mil-DE090-2A1A-3
        0.9%
        156.2M
        41.2%
        100.0%
        aviti_hq | KMS12BM.deepvariant
        6032754
        4932612
        1104333
        1.45
        aviti_hq | KMS12BM.md
        0.45%
        0.0M
        1076.3M
        99.9%
        98.6%
        1077.7M
        82.0%
        52.0X
        51.4
        aviti_hq | MM1S-20231208_PLT-04_EBSL-0477-OBPA_MM1-S-5mil-DE090-3A1A-1
        1.0%
        168.1M
        41.0%
        100.0%
        aviti_hq | MM1S-20231208_PLT-04_EBSL-0477-OBPA_MM1-S-5mil-DE090-3A1A-2
        1.0%
        172.6M
        41.0%
        100.0%
        aviti_hq | MM1S-20231208_PLT-04_EBSL-0477-OBPA_MM1-S-5mil-DE090-3A1A-3
        1.1%
        185.9M
        40.9%
        100.0%
        aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-1
        1.0%
        167.3M
        41.0%
        100.0%
        aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-2
        1.0%
        171.8M
        41.0%
        100.0%
        aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-2.MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-2_1
        1.4%
        aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-3
        1.1%
        185.0M
        40.9%
        100.0%
        aviti_hq | MM1S.deepvariant
        7310096
        5972578
        1345142
        1.52
        aviti_hq | MM1S.md
        0.49%
        0.0M
        1049.3M
        99.9%
        98.5%
        1050.7M
        84.0%
        52.0X
        49.9
        aviti_hq | OPM2-20231208_PLT-04_EBSL-0477-OBPA_5mil-OPM2-DE090-1A1A-1
        1.0%
        168.0M
        41.0%
        100.0%
        aviti_hq | OPM2-20231208_PLT-04_EBSL-0477-OBPA_5mil-OPM2-DE090-1A1A-2
        1.2%
        207.5M
        41.0%
        100.0%
        aviti_hq | OPM2-20231208_PLT-04_EBSL-0477-OBPA_5mil-OPM2-DE090-1A1A-3
        0.9%
        152.0M
        40.9%
        100.0%
        aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-1
        1.0%
        167.3M
        41.0%
        100.0%
        aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-2
        1.2%
        206.2M
        41.0%
        100.0%
        aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-3
        0.9%
        151.4M
        41.0%
        100.0%
        aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-3.OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-3_1
        1.2%
        aviti_hq | OPM2.deepvariant
        6258119
        5100554
        1162788
        1.46
        aviti_hq | OPM2.md
        0.46%
        0.0M
        1051.2M
        99.9%
        98.7%
        1052.4M
        87.0%
        49.0X
        50.1
        aviti_hq | REH-20231208_PLT-04_EBSL-0477-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1
        0.9%
        158.0M
        41.0%
        100.0%
        aviti_hq | REH-20231208_PLT-04_EBSL-0477-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2
        0.9%
        152.4M
        41.0%
        100.0%
        aviti_hq | REH-20231208_PLT-04_EBSL-0477-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3
        1.0%
        166.2M
        41.0%
        100.0%
        aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1
        0.9%
        156.7M
        41.1%
        100.0%
        aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1.REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1_1
        1.1%
        aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2
        0.9%
        151.2M
        41.0%
        100.0%
        aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3
        1.0%
        165.0M
        41.0%
        100.0%
        aviti_hq | REH.deepvariant
        7318664
        5547147
        1781287
        1.52
        aviti_hq | REH.md
        0.51%
        0.0M
        948.3M
        99.9%
        98.4%
        949.6M
        86.0%
        47.0X
        45.3
        aviti_ngi | KMS12BM-B2403418434_KMS-812-5mil-DE090-2A1A-1
        1.0%
        145.5M
        41.3%
        100.0%
        aviti_ngi | KMS12BM-B2403418435_KMS-812-5mil-DE090-2A1A-2
        0.9%
        122.2M
        41.4%
        100.0%
        aviti_ngi | KMS12BM-B2403418436_KMS-812-5mil-DE090-2A1A-3
        0.8%
        112.6M
        41.4%
        100.0%
        aviti_ngi | KMS12BM-B2403418436_KMS-812-5mil-DE090-2A1A-3.KMS12BM-B2403418436_KMS-812-5mil-DE090-2A1A-3_1
        3.4%
        aviti_ngi | KMS12BM.deepvariant
        5472867
        4503819
        972799
        1.55
        aviti_ngi | KMS12BM.md
        0.38%
        0.0M
        379.7M
        99.9%
        98.6%
        380.3M
        7.0%
        17.0X
        17.7
        aviti_ngi | MM1S-B2403418437_MM1-S-5mil-DE090-3A1A-1
        0.8%
        122.2M
        41.1%
        100.0%
        aviti_ngi | MM1S-B2403418437_MM1-S-5mil-DE090-3A1A-1.MM1S-B2403418437_MM1-S-5mil-DE090-3A1A-1_1
        3.7%
        aviti_ngi | MM1S-B2403418438_MM1-S-5mil-DE090-3A1A-2
        0.9%
        124.9M
        41.1%
        100.0%
        aviti_ngi | MM1S-B2403418439_MM1-S-5mil-DE090-3A1A-3
        0.9%
        135.3M
        41.1%
        100.0%
        aviti_ngi | MM1S.deepvariant
        6699677
        5519015
        1187308
        1.62
        aviti_ngi | MM1S.md
        0.42%
        0.0M
        381.8M
        99.9%
        98.5%
        382.4M
        4.0%
        18.0X
        17.7
        aviti_ngi | OPM2-B2403418431_5mil-OPM2-DE090-1A1A-1
        1.0%
        143.6M
        41.2%
        100.0%
        aviti_ngi | OPM2-B2403418431_5mil-OPM2-DE090-1A1A-1.OPM2-B2403418431_5mil-OPM2-DE090-1A1A-1_1
        3.6%
        aviti_ngi | OPM2-B2403418432_5mil-OPM2-DE090-1A1A-2
        0.9%
        146.7M
        41.2%
        100.0%
        aviti_ngi | OPM2-B2403418433_5mil-OPM2-DE090-1A1A-3
        1.0%
        140.9M
        41.2%
        100.0%
        aviti_ngi | OPM2.deepvariant
        5741130
        4712295
        1033437
        1.55
        aviti_ngi | OPM2.md
        0.37%
        0.0M
        430.7M
        99.9%
        98.7%
        431.2M
        9.0%
        20.0X
        20.0
        aviti_ngi | REH-B2403418440_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1
        0.7%
        103.8M
        41.3%
        100.0%
        aviti_ngi | REH-B2403418440_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1.REH-B2403418440_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1_1
        3.2%
        aviti_ngi | REH-B2403418441_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2
        0.8%
        115.5M
        41.2%
        100.0%
        aviti_ngi | REH-B2403418442_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3
        0.7%
        107.1M
        41.3%
        100.0%
        aviti_ngi | REH.deepvariant
        6541810
        5068647
        1481122
        1.63
        aviti_ngi | REH.md
        0.43%
        0.0M
        325.9M
        99.8%
        98.3%
        326.4M
        1.0%
        15.0X
        15.2
        xplus_sns | KMS12BM-L001_Sample_FU-199-KMS-812-5mil-DE090-2A1A
        18.7%
        260.4M
        41.0%
        100.0%
        xplus_sns | KMS12BM-L001_Sample_FU-199-KMS-812-5mil-DE090-2A1A_1
        19.0%
        244.0M
        41.1%
        100.0%
        xplus_sns | KMS12BM-L001_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2
        18.5%
        203.3M
        41.1%
        100.0%
        xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A
        7.1%
        249.8M
        40.9%
        100.0%
        xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_1
        7.2%
        234.7M
        40.9%
        100.0%
        xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2
        6.8%
        196.6M
        40.9%
        100.0%
        xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2.KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2_1
        21.3%
        xplus_sns | KMS12BM.deepvariant
        7153111
        6014299
        1142866
        1.44
        xplus_sns | KMS12BM.md
        0.49%
        0.0M
        1384.5M
        99.7%
        97.9%
        1388.8M
        83.0%
        53.0X
        52.4
        xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A
        19.7%
        231.4M
        40.9%
        100.0%
        xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A.MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A_1
        22.6%
        xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A_1
        17.7%
        220.0M
        40.9%
        100.0%
        xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A_2
        20.8%
        255.1M
        40.9%
        100.0%
        xplus_sns | MM1S-L002_Sample_FU-199-MM1-S-5mil-DE090-3A1A
        7.4%
        222.6M
        40.7%
        100.0%
        xplus_sns | MM1S-L002_Sample_FU-199-MM1-S-5mil-DE090-3A1A_1
        6.5%
        210.2M
        40.7%
        100.0%
        xplus_sns | MM1S-L002_Sample_FU-199-MM1-S-5mil-DE090-3A1A_2
        7.9%
        235.2M
        40.7%
        100.0%
        xplus_sns | MM1S.deepvariant
        8386268
        7025410
        1368178
        1.52
        xplus_sns | MM1S.md
        0.54%
        0.0M
        1370.9M
        99.7%
        97.8%
        1374.5M
        85.0%
        52.0X
        51.0
        xplus_sns | OPM2-L001_Sample_FU-199-5mil-OPM2-DE090-1A1A
        21.4%
        282.0M
        41.0%
        100.0%
        xplus_sns | OPM2-L001_Sample_FU-199-5mil-OPM2-DE090-1A1A_1
        20.1%
        276.2M
        40.9%
        100.0%
        xplus_sns | OPM2-L001_Sample_FU-199-5mil-OPM2-DE090-1A1A_2
        18.7%
        243.0M
        41.0%
        100.0%
        xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A
        8.4%
        272.1M
        40.8%
        100.0%
        xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_1
        7.7%
        264.3M
        40.8%
        100.0%
        xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_2
        7.1%
        236.1M
        40.8%
        100.0%
        xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_2.OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_2_1
        22.6%
        xplus_sns | OPM2.deepvariant
        7310984
        6119230
        1196491
        1.44
        xplus_sns | OPM2.md
        0.48%
        0.0M
        1569.5M
        99.7%
        98.1%
        1573.8M
        91.0%
        57.0X
        58.4
        xplus_sns | REH-L001_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1
        19.6%
        207.4M
        41.0%
        100.0%
        xplus_sns | REH-L001_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2
        22.0%
        246.2M
        41.0%
        100.0%
        xplus_sns | REH-L001_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3
        20.1%
        213.1M
        41.0%
        100.0%
        xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1
        7.2%
        190.5M
        40.8%
        100.0%
        xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1.REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1_1
        24.0%
        xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2
        8.4%
        227.2M
        40.7%
        100.0%
        xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3
        7.4%
        197.1M
        40.8%
        100.0%
        xplus_sns | REH.deepvariant
        8055071
        6266749
        1797390
        1.57
        xplus_sns | REH.md
        0.54%
        0.0M
        1277.9M
        99.7%
        97.6%
        1281.6M
        87.0%
        48.0X
        46.7

        FastP (Read preprocessing)

        FastP (Read preprocessing) An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC

        GATK4 MarkDuplicates

        GATK4 MarkDuplicates metrics generated either by GATK4 MarkDuplicates or EstimateLibraryComplexity (with --use_gatk_spark).

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        Created with MultiQC

        Samtools Flagstat

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Created with MultiQC

        Mosdepth

        Mosdepth performs fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.DOI: 10.1093/bioinformatics/btx699.

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 12 samples, a BED file was provided, so the data was calculated across those regions. For 12 samples, it's calculated across the entire genome length. 12 samples have both global and region reports, and we are showing the data for regions

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Coverage distribution

        Proportion of bases in the reference genome with a given depth of coverage. Note that for 12 samples, a BED file was provided, so the data was calculated across those regions. For 12 samples, it's calculated across the entire genome length. 12 samples have both global and region reports, and we are showing the data for regions

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

        Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

        If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

        This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        XY coverage

        Created with MultiQC

        Bcftools

        Bcftools contains utilities for variant calling and manipulating VCFs and BCFs.DOI: 10.1093/gigascience/giab008.

        Variant Substitution Types

        Created with MultiQC

        Variant Quality

        Created with MultiQC

        Indel Distribution

        Created with MultiQC

        Vcftools

        Vcftools is a program for working with and reporting on VCF files.DOI: 10.1093/bioinformatics/btr330.

        TsTv by Count

        Plot of TSTV-BY-COUNT - the transition to transversion ratio as a function of alternative allele count from the output of vcftools TsTv-by-count.

        Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations. Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation. Alternative allele count is the number of alternative alleles at the site. Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.) Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-count

        Created with MultiQC

        TsTv by Qual

        Plot of TSTV-BY-QUAL - the transition to transversion ratio as a function of SNP quality from the output of vcftools TsTv-by-qual.

        Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations. Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation. Quality here is the Phred-scaled quality score as given in the QUAL column of VCF. Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.) Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-qual

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        BCFTOOLS_STATSbcftools1.18
        BWAMEM1_MEMbwa0.7.17.post1188
        samtools1.19.2
        DEEPVARIANTdeepvariant1.5.0
        FASTPfastp0.23.4
        GATK4 MarkDuplicatesgatk44.5.0.0
        samtools1.19.2
        MERGE_DEEPVARIANT_GVCFgatk44.5.0.0
        MERGE_DEEPVARIANT_VCFgatk44.5.0.0
        Mosdepthmosdepth0.3.8
        SAMTOOLS_STATSsamtools1.19.2
        VCFTOOLS_TSTV_COUNTvcftools0.1.16
        WorkflowNextflow24.4.2
        nf-core/sarek3.4.2

        nf-core/sarek Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using nf-core/sarek v3.4.2 (doi: 10.12688/f1000research.16665.2), (doi: 10.1093/nargab/lqae031), (doi: 10.5281/zenodo.3476425) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.04.1 (Di Tommaso et al., 2017) with the following command:

        nextflow run /vulpes/ngi/production/v24.07/sw/sarek/3_4_2/ -profile uppmax --project ngi2016004 -c /vulpes/ngi/production/v24.07/conf/sarek_sthlm.config -c ../nextflow.config -params-file aviti_hq_params.yaml -resume

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/sarek Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        runName
        tender_venter
        containerEngine
        singularity
        launchDir
        /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/nfcore_sarek_rerun/aviti_hq
        workDir
        /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/nfcore_sarek_rerun/aviti_hq/work
        projectDir
        /vulpes/ngi/production/v24.07/sw/sarek/3_4_2
        userName
        phojer
        profile
        uppmax
        configFiles
        N/A

        Input/output options

        input
        aviti_hq_samples.csv
        outdir
        outdir

        Main options

        tools
        deepvariant,cnvkit
        skip_tools
        baserecalibrator,fastqc

        FASTQ Preprocessing

        trim_fastq
        true

        Reference genome options

        bwa
        /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/resources/GRCh38_GIABv3/unbgzipped/
        fasta
        /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/resources/GRCh38_GIABv3/unbgzipped/genome.fa
        fasta_fai
        /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/resources/GRCh38_GIABv3/unbgzipped/genome.fa.fai
        save_reference
        true
        igenomes_base
        /sw/data/igenomes/
        igenomes_ignore
        true

        Institutional config options

        custom_config_base
        /vulpes/ngi/production/v24.07/sw/sarek/3_4_2/../configs/
        config_profile_description
        nf-core/sarek uppmax profile provided by nf-core/configs
        config_profile_contact
        Maxime Garcia (@MaxUlysse)
        config_profile_url
        https://www.uppmax.uu.se/
        seq_center
        Element_Biosciences_San_Diego
        seq_platform
        ELEMENT

        Max job request options

        max_cpus
        48
        max_memory
        357 GB
        max_time
        20d

        Generic options

        email
        pontus.hojer@scilifelab.se
        validationLenientMode
        true